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Local Factor Models for Large-Scale Inductive Recommendation

Published: 13 September 2021 Publication History

Abstract

In many domains, user preferences are similar locally within like-minded subgroups of users, but typically differ globally between those subgroups. Local recommendation models were shown to substantially improve top-K recommendation performance in such settings. However, existing local models do not scale to large-scale datasets with an increasing number of subgroups and do not support inductive recommendations for users not appearing in the training set. Key reasons for this are that subgroup detection and recommendation get implemented as separate steps in the model or that local models are explicitly instantiated for each subgroup. In this paper, we propose an End-to-end Local Factor Model (Elfm) which overcomes these limitations by combining both steps and incorporating local structures through an inductive bias. Our model can be optimized end-to-end and supports incremental inference, does not require a full separate model for each subgroup, and has overall small memory and computational costs for incorporating local structures. Empirical results show that our method substantially improves recommendation performance on large-scale datasets with millions of users and items with considerably smaller model size. Our user study also shows that our approach produces coherent item subgroups which could aid in the generation of explainable recommendations.

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References

[1]
2006. Yahoo! Webscope dataset ydata-ymusic-rating-study-v1. http://research.yahoo.com/Academic_Relations
[2]
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: A system for large-scale machine learning. In USENIX Symposium on Operating Systems Design and Implementation.
[3]
Eytan Adar, Jaime Teevan, and Susan T Dumais. 2008. Large scale analysis of web revisitation patterns. In SIGCHI conference on Human Factors in Computing Systems.
[4]
Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, Srujana Merugu, and Dharmendra S Modha. 2007. A generalized maximum entropy approach to bregman co-clustering and matrix approximation. Journal of Machine Learning Research(2007).
[5]
James Bennett, Stan Lanning, 2007. The netflix prize. In Proceedings of KDD Cup and Workshop. Citeseer.
[6]
Alex Beutel, Ed H Chi, Zhiyuan Cheng, Hubert Pham, and John Anderson. 2017. Beyond globally optimal: Focused learning for improved recommendations. In International Conference on World Wide Web.
[7]
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research(2003).
[8]
John S Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Conference on Uncertainty in Artificial Intelligence.
[9]
Jiajun Bu, Xin Shen, Bin Xu, Chun Chen, Xiaofei He, and Deng Cai. 2016. Improving collaborative recommendation via user-item subgroups. IEEE Transactions on Knowledge and Data Engineering (2016).
[10]
Rich Caruana, Steve Lawrence, and C Lee Giles. 2001. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. In Advances in Neural Information Processing Systems.
[11]
O. Celma. 2010. Music Recommendation and Discovery in the Long Tail. Springer.
[12]
Jonathan Chang, Sean Gerrish, Chong Wang, Jordan L Boyd-Graber, and David M Blei. 2009. Reading tea leaves: How humans interpret topic models. In Advances in Neural Information Processing Systems.
[13]
Zhengdao Chen, Lisha Li, and Joan Bruna. 2019. Supervised Community Detection with Line Graph Neural Networks. In International Conference on Learning Representations.
[14]
Evangelia Christakopoulou and George Karypis. 2016. Local item-item models for top-n recommendation. In ACM Conference on Recommender Systems.
[15]
Evangelia Christakopoulou and George Karypis. 2018. Local latent space models for top-n recommendation. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
[16]
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In ACM Conference on Recommender Systems.
[17]
Scott Deerwester, Susan T Dumais, George W Furnas, Thomas K Landauer, and Richard Harshman. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science (1990).
[18]
Meghana Deodhar, Gunjan Gupta, Joydeep Ghosh, Hyuk Cho, and Inderjit Dhillon. 2009. A scalable framework for discovering coherent co-clusters in noisy data. In International Conference on Machine Learning.
[19]
Inderjit S Dhillon, Subramanyam Mallela, and Dharmendra S Modha. 2003. Information-theoretic co-clustering. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[20]
Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In ACM SIGIR Conference on Research & Development in Information Retrieval.
[21]
Michael D Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness. In Conference on Fairness, Accountability and Transparency.
[22]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems.
[23]
Per Christian Hansen. 1987. The truncatedsvd as a method for regularization. BIT Numerical Mathematics(1987).
[24]
F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (2015).
[25]
Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In AAAI Conference on Artificial Intelligence.
[26]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In International Conference on World Wide Web.
[27]
Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In ACM SIGIR Conference on Research and Development in Information Retrieval.
[28]
Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative metric learning. In International Conference on World Wide Web.
[29]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In IEEE International Conference on Data Mining.
[30]
Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015. On Using Very Large Target Vocabulary for Neural Machine Translation. In Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing.
[31]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In IEEE International Conference on Data Mining.
[32]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).
[33]
Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2014. Local collaborative ranking. In International Conference on World Wide Web.
[34]
Joonseok Lee, Seungyeon Kim, Guy Lebanon, and Yoram Singer. 2013. Local low-rank matrix approximation. In International Conference on Machine Learning.
[35]
Jacek Łęski. 2003. Towards a robust fuzzy clustering. Fuzzy Sets and Systems(2003).
[36]
Xiang Li, Ben Kao, Zhaochun Ren, and Dawei Yin. 2019. Spectral clustering in heterogeneous information networks. In AAAI Conference on Artificial Intelligence.
[37]
Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang. 2015. Large-scale multi-view spectral clustering via bipartite graph. In AAAI Conference on Artificial Intelligence.
[38]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In International Conference on World Wide Web.
[39]
Dongsheng Luo, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu, and Xiang Zhang. 2020. Deep Multi-Graph Clustering via Attentive Cross-Graph Association. In ACM International Conference on Web Search and Data Mining.
[40]
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning Disentangled Representations for Recommendation. In Advances in Neural Information Processing Systems.
[41]
Benjamin M Marlin. 2004. Modeling user rating profiles for collaborative filtering. In Advances in Neural Information Processing Systems.
[42]
Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In International Conference on Machine Learning.
[43]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research(2011).
[44]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Conference on Uncertainty in Artificial Intelligence.
[45]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In International Conference on World Wide Web.
[46]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems.
[47]
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[48]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In ACM SIGIR Conference on Research and Development in Information Retrieval.
[49]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In ACM SIGIR Conference on Research and Development in Information Retrieval.
[50]
Hongyi Wen, Longqi Yang, Michael Sobolev, and Deborah Estrin. 2018. Exploring recommendations under user-controlled data filtering. In ACM Conference on Recommender Systems.
[51]
Joyce Jiyoung Whang and Inderjit S. Dhillon. 2017. Non-Exhaustive, Overlapping Co-Clustering. In ACM on Conference on Information and Knowledge Management.
[52]
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017. Recurrent recommender networks. In ACM International Conference on Web Search and Data Mining.
[53]
Yao Wu, Xudong Liu, Min Xie, Martin Ester, and Qing Yang. 2016. Cccf: Improving collaborative filtering via scalable user-item co-clustering. In ACM International Conference on Web Search and Data Mining.
[54]
Yaowei Yan, Yuchen Bian, Dongsheng Luo, Dongwon Lee, and Xiang Zhang. 2019. Constrained local graph clustering by colored random walk. In The World Wide Web Conference.
[55]
Jaewon Yang and Jure Leskovec. 2012. Community-affiliation graph model for overlapping network community detection. In IEEE International Conference on Data Mining.
[56]
Jaewon Yang and Jure Leskovec. 2013. Overlapping community detection at scale: a nonnegative matrix factorization approach. In ACM International Conference on Web Search and Data Mining.
[57]
Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, and Deborah Estrin. 2018. Openrec: A modular framework for extensible and adaptable recommendation algorithms. In ACM International Conference on Web Search and Data Mining.
[58]
Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. In Advances in Neural Information Processing Systems.
[59]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

Cited By

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  • (2024)Evaluating Deep Learning and Traditional Approaches in Recommender Systems2024 8th International Conference on Information Technology (InCIT)10.1109/InCIT63192.2024.10810580(399-404)Online publication date: 14-Nov-2024
  • (2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
  • (2023)Targeted Training for Multi-organization RecommendationACM Transactions on Recommender Systems10.1145/36035081:3(1-18)Online publication date: 14-Jul-2023
  • Show More Cited By

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cover image ACM Conferences
RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems
September 2021
883 pages
ISBN:9781450384582
DOI:10.1145/3460231
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 September 2021

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Author Tags

  1. Recommendation
  2. end-to-end
  3. large-scale
  4. local model

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  • Research-article
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  • Refereed limited

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RecSys '21: Fifteenth ACM Conference on Recommender Systems
September 27 - October 1, 2021
Amsterdam, Netherlands

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2024)Evaluating Deep Learning and Traditional Approaches in Recommender Systems2024 8th International Conference on Information Technology (InCIT)10.1109/InCIT63192.2024.10810580(399-404)Online publication date: 14-Nov-2024
  • (2023)Collaborative filtering algorithms are prone to mainstream-taste biasProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608825(750-756)Online publication date: 14-Sep-2023
  • (2023)Targeted Training for Multi-organization RecommendationACM Transactions on Recommender Systems10.1145/36035081:3(1-18)Online publication date: 14-Jul-2023
  • (2023)Editable User Profiles for Controllable Text RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591677(993-1003)Online publication date: 19-Jul-2023

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